We study distributed learning with the least squares regularization scheme in a reproducing
kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a …

Spectral algorithms have been widely used and studied in learning theory and inverse
problems. This paper is concerned with distributed spectral algorithms, for handling big data …

This paper provides error analysis for distributed semi-supervised learning with kernel ridge
regression (DSKRR) based on a divide-and-conquer strategy. DSKRR applies kernel ridge …

Neural networks activated by the rectified linear unit (ReLU) play a central role in the recent
development of deep learning. The topic of approximating functions from Hölder spaces by …

Convolutional neural networks (CNNs) have gained immense popularity in recent years,
finding their utility in diverse fields such as image recognition, natural language processing …

Pairwise learning refers to learning tasks where the loss function depends on a pair of
instances. It instantiates many important machine learning tasks such as bipartite ranking …

Recently, there is a growing interest in studying pairwise learning since it includes many
important machine learning tasks as specific examples, eg, metric learning, AUC …

Within the statistical learning framework, this paper studies the regression model associated
with the correntropy induced losses. The correntropy, as a similarity measure, has been …

We study the generalization ability of distributed learning equipped with a divide-and-
conquer approach and gradient descent algorithm in a reproducing kernel Hilbert space …

Pairwise learning refers to learning tasks with loss functions depending on a pair of training
examples, which includes ranking and metric learning as specific examples. Recently, there …